1,207 research outputs found
An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks
Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs
Optimal Deployments of UAVs With Directional Antennas for a Power-Efficient Coverage
To provide a reliable wireless uplink for users in a given ground area, one
can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another
application, one can use UAVs to collect data from sensors on the ground. For a
power-efficient and scalable deployment of such flying BSs, directional
antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We
consider a large-scale wireless path-loss model with a realistic
angle-dependent radiation pattern for the directional antennas. Based on such a
model, we determine the optimal 3-D deployment of N UAVs to minimize the
average transmit-power consumption of the users in a given target area. The
users are assumed to have identical transmitters with ideal omnidirectional
antennas and the UAVs have identical directional antennas with given half-power
beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For
uniformly distributed ground users, we show that the UAVs have to share a
common flight height in an optimal power-efficient deployment. We also derive
in closed-form the asymptotic optimal common flight height of UAVs in terms
of the area size, data-rate, bandwidth, HPBW, and path-loss exponent
CDAR : contour detection aggregation and routing in sensor networks
Wireless sensor networks offer the advantages of low cost, flexible measurement of phenomenon in a wide variety of applications, and easy deployment. Since sensor nodes are typically battery powered, energy efficiency is an important objective in designing sensor network algorithms. These algorithms are often application-specific, owing to the need to carefully optimize energy usage, and since deployments usually support a single or very few applications.
This thesis concerns applications in which the sensors monitor a continuous scalar field, such as temperature, and addresses the problem of determining the location of a contour line in this scalar field, in response to a query, and communicating this information to a designated sink node. An energy-efficient solution to this problem is proposed and evaluated. This solution includes new contour detection and query propagation algorithms, in-network-processing algorithms, and routing algorithms. Only a small fraction of network nodes may be adjacent to the desired contour line, and the contour detection and query propagation algorithms attempt to minimize processing and communication by the other network nodes. The in-network processing algorithms reduce communication volume through suppression, compression and aggregation techniques. Finally, the routing algorithms attempt to route the contour information to the sink as efficiently as possible, while meshing with the other algorithms. Simulation results show that the proposed algorithms yield significant improvements in data and message volumes compared to baseline models, while maintaining the integrity of the contour representation
Particle Swarm Optimization for the Clustering of Wireless Sensors
Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a \u27swarm\u27 of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network
Clustered wireless sensor networks
The study of topology in randomly deployed wireless sensor networks (WSNs) is important in addressing the fundamental issue of stochastic coverage resulting from randomness in the deployment procedure and power management algorithms. This dissertation defines and studies clustered WSNs, WSNs whose topology due to the deployment procedure and the application requirements results in the phenomenon of clustering or clumping of nodes. The first part of this dissertation analyzes a range of topologies of clustered WSNs and their impact on the primary sensing objectives of coverage and connectivity. By exploiting the inherent advantages of clustered topologies of nodes, this dissertation presents techniques for optimizing the primary performance metrics of power consumption and network capacity. It analyzes clustering in the presence of obstacles, and studies varying levels of redundancy to determine the probability of coverage in the network. The proposed models for clustered WSNs embrace the domain of a wide range of topologies that are prevalent in actual real-world deployment scenarios, and call for clustering-specific protocols to enhance network performance. It has been shown that power management algorithms tailored to various clustering scenarios optimize the level of active coverage and maximize the network lifetime. The second part of this dissertation addresses the problem of edge effects and heavy traffic on queuing in clustered WSNs. In particular, an admission control model called directed ignoring model has been developed that aims to minimize the impact of edge effects in queuing by improving queuing metrics such as packet loss and wait time
Wireless Monitoring Systems for Long-Term Reliability Assessment of Bridge Structures based on Compressed Sensing and Data-Driven Interrogation Methods.
The state of the nation’s highway bridges has garnered significant public attention due to large inventories of aging assets and insufficient funds for repair. Current management methods are based on visual inspections that have many known limitations including reliance on surface evidence of deterioration and subjectivity introduced by trained inspectors. To address the limitations of current inspection practice, structural health monitoring (SHM) systems can be used to provide quantitative measures of structural behavior and an objective basis for condition assessment. SHM systems are intended to be a cost effective monitoring technology that also automates the processing of data to characterize damage and provide decision information to asset managers. Unfortunately, this realization of SHM systems does not currently exist. In order for SHM to be realized as a decision support tool for bridge owners engaged in performance- and risk-based asset management, technological hurdles must still be overcome.
This thesis focuses on advancing wireless SHM systems. An innovative wireless monitoring system was designed for permanent deployment on bridges in cold northern climates which pose an added challenge as the potential for solar harvesting is reduced and battery charging is slowed. First, efforts advancing energy efficient usage strategies for WSNs were made. With WSN energy consumption proportional to the amount of data transmitted, data reduction strategies are prioritized. A novel data compression paradigm termed compressed sensing is advanced for embedment in a wireless sensor microcontroller. In addition, fatigue monitoring algorithms are embedded for local data processing leading to dramatic data reductions. In the second part of the thesis, a radical top-down design strategy (in contrast to global vibration strategies) for a monitoring system is explored to target specific damage concerns of bridge owners. Data-driven algorithmic approaches are created for statistical performance characterization of long-term bridge response. Statistical process control and reliability index monitoring are advanced as a scalable and autonomous means of transforming data into information relevant to bridge risk management. Validation of the wireless monitoring system architecture is made using the Telegraph Road Bridge (Monroe, Michigan), a multi-girder short-span highway bridge that represents a major fraction of the U.S. national inventory.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116749/1/ocosean_1.pd
New Secure IoT Architectures, Communication Protocols and User Interaction Technologies for Home Automation, Industrial and Smart Environments
Programa Oficial de Doutoramento en Tecnoloxías da Información e das Comunicacións en Redes Móbiles. 5029V01Tese por compendio de publicacións[Abstract]
The Internet of Things (IoT) presents a communication network where heterogeneous
physical devices such as vehicles, homes, urban infrastructures or industrial machinery
are interconnected and share data. For these communications to be successful, it is
necessary to integrate and embed electronic devices that allow for obtaining environmental
information (sensors), for performing physical actuations (actuators) as well as
for sending and receiving data (network interfaces).
This integration of embedded systems poses several challenges. It is needed for these
devices to present very low power consumption. In many cases IoT nodes are powered by
batteries or constrained power supplies. Moreover, the great amount of devices needed in
an IoT network makes power e ciency one of the major concerns of these deployments,
due to the cost and environmental impact of the energy consumption. This need for low
energy consumption is demanded by resource constrained devices, con
icting with the
second major concern of IoT: security and data privacy. There are critical urban and
industrial systems, such as tra c management, water supply, maritime control, railway
control or high risk industrial manufacturing systems such as oil re neries that will
obtain great bene ts from IoT deployments, for which non-authorized access can posse
severe risks for public safety. On the other hand, both these public systems and the
ones deployed on private environments (homes, working places, malls) present a risk for
the privacy and security of their users. These IoT deployments need advanced security
mechanisms, both to prevent access to the devices and to protect the data exchanged
by them.
As a consequence, it is needed to improve two main aspects: energy e ciency of IoT
devices and the use of lightweight security mechanisms that can be implemented by
these resource constrained devices but at the same time guarantee a fair degree of
security.
The huge amount of data transmitted by this type of networks also presents another
challenge. There are big data systems capable of processing large amounts of data,
but with IoT the granularity and dispersion of the generated information presents a
new scenario very di erent from the one existing nowadays. Forecasts anticipate that there will be a growth from the 15 billion installed devices in 2015 to more than 75
billion devices in 2025. Moreover, there will be much more services exploiting the data
produced by these networks, meaning the resulting tra c will be even higher. The
information must not only be processed in real time, but data mining processes will
have to be performed to historical data.
The main goal of this Ph.D. thesis is to analyze each one of the previously described
challenges and to provide solutions that allow for an adequate adoption of IoT in
Industrial, domestic and, in general, any scenario that can obtain any bene t from the
interconnection and
exibility that IoT brings.[Resumen]
La internet de las cosas (IoT o Internet of Things) representa una red de intercomunicaciones
en la que participan dispositivos físicos de toda índole, como vehículos,
viviendas, electrodomésticos, infraestructuras urbanas o maquinaria y dispositivos industriales.
Para que esta comunicación se pueda llevar a cabo es necesario integrar
elementos electr onicos que permitan obtener informaci on del entorno (sensores), realizar
acciones f sicas (actuadores) y enviar y recibir la informaci on necesaria (interfaces de
comunicaciones de red).
La integración y uso de estos sistemas electrónicos embebidos supone varios retos. Es
necesario que dichos dispositivos presenten un consumo reducido. En muchos casos
deberían ser alimentados por baterías o fuentes de alimentación limitadas. Además,
la gran cantidad de dispositivos que involucra la IoT hace necesario que la e ciencia
energética de los mismos sea una de las principales preocupaciones, por el coste e
implicaciones medioambientales que supone el consumo de electricidad de los mismos.
Esta necesidad de limitar el consumo provoca que dichos dispositivos tengan unas
prestaciones muy limitadas, lo que entra en conflicto con la segunda mayor preocupación
de la IoT: la seguridad y privacidad de los datos. Por un lado existen sistemas críticos
urbanos e industriales, como puede ser la regulación del tráfi co, el control del suministro
de agua, el control marítimo, el control ferroviario o los sistemas de producción industrial
de alto riesgo, como refi nerías, que son claros candidatos a benefi ciarse de la IoT, pero
cuyo acceso no autorizado supone graves problemas de seguridad ciudadana. Por otro
lado, tanto estos sistemas de naturaleza publica, como los que se desplieguen en entornos
privados (viviendas, entornos de trabajo o centros comerciales, entre otros) suponen
un riesgo para la privacidad y también para la seguridad de los usuarios. Todo esto
hace que sean necesarios mecanismos de seguridad avanzados, tanto de acceso a los
dispositivos como de protección de los datos que estos intercambian.
En consecuencia, es necesario avanzar en dos aspectos principales: la e ciencia energética de los dispositivos y el uso de mecanismos de seguridad e ficientes, tanto
computacional como energéticamente, que permitan la implantación de la IoT sin
comprometer la seguridad y la privacidad de los usuarios. Por otro lado, la ingente cantidad de información que estos sistemas puede llegar
a producir presenta otros dos retos que deben ser afrontados. En primer lugar, el
tratamiento y análisis de datos toma una nueva dimensión. Existen sistemas de big
data capaces de procesar cantidades enormes de información, pero con la internet de
las cosas la granularidad y dispersión de los datos plantean un escenario muy distinto
al actual. La previsión es pasar de 15.000.000.000 de dispositivos instalados en 2015
a más de 75.000.000.000 en 2025. Además existirán multitud de servicios que harán
un uso intensivo de estos dispositivos y de los datos que estos intercambian, por lo
que el volumen de tráfico será todavía mayor. Asimismo, la información debe ser
procesada tanto en tiempo real como a posteriori sobre históricos, lo que permite
obtener información estadística muy relevante en diferentes entornos.
El principal objetivo de la presente tesis doctoral es analizar cada uno de estos retos
(e ciencia energética, seguridad, procesamiento de datos e interacción con el usuario)
y plantear soluciones que permitan una correcta adopción de la internet de las cosas
en ámbitos industriales, domésticos y en general en cualquier escenario que se pueda
bene ciar de la interconexión y
flexibilidad de acceso que proporciona el IoT.[Resumo]
O internet das cousas (IoT ou Internet of Things) representa unha rede de intercomunicaci
óns na que participan dispositivos físicos moi diversos, coma vehículos, vivendas,
electrodomésticos, infraestruturas urbanas ou maquinaria e dispositivos industriais.
Para que estas comunicacións se poidan levar a cabo é necesario integrar elementos
electrónicos que permitan obter información da contorna (sensores), realizar accións
físicas (actuadores) e enviar e recibir a información necesaria (interfaces de comunicacións
de rede).
A integración e uso destes sistemas electrónicos integrados supón varios retos. En
primeiro lugar, é necesario que estes dispositivos teñan un consumo reducido. En
moitos casos deberían ser alimentados por baterías ou fontes de alimentación limitadas.
Ademais, a gran cantidade de dispositivos que se empregan na IoT fai necesario que a
e ciencia enerxética dos mesmos sexa unha das principais preocupacións, polo custo e
implicacións medioambientais que supón o consumo de electricidade dos mesmos. Esta
necesidade de limitar o consumo provoca que estes dispositivos teñan unhas prestacións
moi limitadas, o que entra en con
ito coa segunda maior preocupación da IoT: a
seguridade e privacidade dos datos. Por un lado existen sistemas críticos urbanos e
industriais, como pode ser a regulación do tráfi co, o control de augas, o control marítimo,
o control ferroviario ou os sistemas de produción industrial de alto risco, como refinerías,
que son claros candidatos a obter benefi cios da IoT, pero cuxo acceso non autorizado
supón graves problemas de seguridade cidadá. Por outra parte tanto estes sistemas de
natureza pública como os que se despreguen en contornas privadas (vivendas, contornas
de traballo ou centros comerciais entre outros) supoñen un risco para a privacidade e
tamén para a seguridade dos usuarios. Todo isto fai que sexan necesarios mecanismos
de seguridade avanzados, tanto de acceso aos dispositivos como de protección dos datos
que estes intercambian.
En consecuencia, é necesario avanzar en dous aspectos principais: a e ciencia enerxética
dos dispositivos e o uso de mecanismos de seguridade re cientes, tanto computacional
como enerxéticamente, que permitan o despregue da IoT sen comprometer a seguridade
e a privacidade dos usuarios.
Por outro lado, a inxente cantidade de información que estes sistemas poden chegar
a xerar presenta outros retos que deben ser tratados. O tratamento e a análise de
datos toma unha nova dimensión. Existen sistemas de big data capaces de procesar
cantidades enormes de información, pero coa internet das cousas a granularidade e
dispersión dos datos supón un escenario moi distinto ao actual. A previsión e pasar
de 15.000.000.000 de dispositivos instalados no ano 2015 a m ais de 75.000.000.000 de
dispositivos no ano 2025. Ademais existirían multitude de servizos que farían un uso
intensivo destes dispositivos e dos datos que intercambian, polo que o volume de tráfico
sería aínda maior. Do mesmo xeito a información debe ser procesada tanto en tempo
real como posteriormente sobre históricos, o que permite obter información estatística
moi relevante en diferentes contornas.
O principal obxectivo da presente tese doutoral é analizar cada un destes retos
(e ciencia enerxética, seguridade, procesamento de datos e interacción co usuario) e
propor solucións que permitan unha correcta adopción da internet das cousas en ámbitos
industriais, domésticos e en xeral en todo aquel escenario que se poda bene ciar da
interconexión e
flexibilidade de acceso que proporciona a IoT
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